miSTAR : miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structureReport as inadecuate




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(2017)NUCLEIC ACIDS RESEARCH.45(7). Mark abstract In microRNA (miRNA) target prediction, typically two levels of information need to be modeled: the number of potential miRNA binding sites present in a target mRNA and the genomic context of each individual site. Single model structures insufficiently cope with this complex training data structure, consisting of feature vectors of unequal length as a consequence of the varying number of miRNA binding sites in different mRNAs. To circumvent this problem, we developed a two-layered, stacked model, in which the influence of binding site context is separately modeled. Using logistic regression and random forests, we applied the stacked model approach to a unique data set of 7990 probed miRNA-mRNA interactions, hereby including the largest number of miRNAs in model training to date. Compared to lower-complexity models, a particular stacked model, named miSTAR (miRNA stacked model target prediction; www.mi-star.org), displays a higher general performance and precision on top scoring predictions. More importantly, our model outperforms published and widely used miRNA target prediction algorithms. Finally, we highlight flaws in cross-validation schemes for evaluation of miRNA target prediction models and adopt a more fair and stringent approach.

Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8509343



Author: Gert Van Peer, Ayla De Paepe, Michiel Stock , Jasper Anckaert , Pieter-Jan Volders , Jo Vandesompele , Bernard De Baets and Willem

Source: https://biblio.ugent.be/publication/8509343



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